Qiu, Y; Djemili, K; Elezi, D; Shalman, A; Pérez-Orti, M; Bulathwela, S; (2023) TrueLearn: A Python Library for Personalised Informational Recommendations with (Implicit) Feedback. In: Proceedings of the 6th Workshop on Online Recommender Systems and User Modeling, jointly with the 17th ACM Conference on Recommender Systems. (pp. pp. 1-14). CEUR Workshop Proceedings

Preview

PDF paper3.pdf - Published Version Download (1MB) | Preview

Abstract

This work describes the TrueLearn Python library, which contains a family of online learning Bayesian models for building educational (or more generally, informational) recommendation systems. This family of models was designed following the "open learner" concept, using humanly-intuitive user representations. For the sake of interpretability and putting the user in control, the TrueLearn library also contains different representations to help end-users visualise the learner models, which may in the future facilitate user interaction with their own models. Together with the library, we include a previously publicly released implicit feedback educational dataset with evaluation metrics to measure the performance of the models. The extensive documentation and coding examples make the library highly accessible to both machine learning developers and educational data mining and learning analytic practitioners. The library and the support documentation with examples are available at https://truelearn. readthedocs.io/en/latest.

Download activity - last month

Export as XMLCSVJSON

Download activity - last 12 months

Export as XMLCSVJSON

Downloads by country - last 12 months

Export as XMLCSVJSON

Archive Staff Only

View Item